{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a6496b76-fb2e-4445-9ac0-84e3b483fec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import pandas as pd # type: ignore\n",
    "import numpy as np # type: ignore\n",
    "import matplotlib.pyplot as plt # type: ignore\n",
    "from sklearn.model_selection import train_test_split # type: ignore\n",
    "from tqdm import trange # type: ignore\n",
    "\n",
    "import torch # type: ignore\n",
    "from torch import nn # type: ignore\n",
    "from torchvision import transforms # type: ignore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af3a3bdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f3fd740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(            pH值        溶解氧         电导率          浊度    高锰酸盐指数      化学需氧量  \\\n",
       " 0      8.162907  10.382206  372.093235  161.788707  3.994236  16.076021   \n",
       " 1      8.000000  13.200000   12.000000   25.100000  3.700000  14.500000   \n",
       " 2      9.000000  10.600000   46.400000   20.000000  3.700000  15.000000   \n",
       " 3      8.162907  10.382206  372.093235  161.788707  3.994236  16.076021   \n",
       " 4      8.162907  10.382206  372.093235  161.788707  3.994236  16.076021   \n",
       " ...         ...        ...         ...         ...       ...        ...   \n",
       " 11174  8.174938  10.324194  317.872825  148.806188  3.990255  16.031762   \n",
       " 11175  8.174938  10.324194  317.872825  148.806188  3.990255  16.031762   \n",
       " 11176  8.174938  10.324194  317.872825  148.806188  3.990255  16.031762   \n",
       " 11177  8.174938  10.324194  317.872825  148.806188  3.990255  16.031762   \n",
       " 11178  8.174938  10.324194  317.872825  148.806188  3.990255  16.031762   \n",
       " \n",
       "         五日生化需氧量        氨氮         总磷        总氮  ...         镉       六价铬  \\\n",
       " 0      2.555892  0.418878   0.108164  3.658420  ...  0.000096  0.003287   \n",
       " 1      1.500000  0.490000   0.070000  1.970000  ...  0.000050  0.002000   \n",
       " 2      2.200000  0.440000   0.070000  2.230000  ...  0.000050  0.002000   \n",
       " 3      2.555892  0.418878   0.108164  3.658420  ...  0.000096  0.003287   \n",
       " 4      2.555892  0.418878   0.108164  3.658420  ...  0.000096  0.003287   \n",
       " ...         ...       ...        ...       ...  ...       ...       ...   \n",
       " 11174  2.603748  2.643281  10.289133  0.311605  ...  0.000089  0.003413   \n",
       " 11175  2.603748  2.643281  10.289133  0.311605  ...  0.000089  0.003413   \n",
       " 11176  2.603748  2.643281  10.289133  0.311605  ...  0.000089  0.003413   \n",
       " 11177  2.603748  2.643281  10.289133  0.311605  ...  0.000089  0.003413   \n",
       " 11178  2.603748  2.643281  10.289133  0.311605  ...  0.000089  0.003413   \n",
       " \n",
       "               铅       氰化物       挥发酚       石油类  阴离子表面活性剂       硫化物  timediff  \\\n",
       " 0      0.001203  0.001912  0.000662  0.013263  0.031628  0.009240     991.0   \n",
       " 1      0.001000  0.002000  0.000200  0.020000  0.020000  0.005000     991.0   \n",
       " 2      0.001000  0.002000  0.000200  0.005000  0.020000  0.005000     991.0   \n",
       " 3      0.001203  0.001912  0.000662  0.013263  0.031628  0.009240     991.0   \n",
       " 4      0.001203  0.001912  0.000662  0.013263  0.031628  0.009240     991.0   \n",
       " ...         ...       ...       ...       ...       ...       ...       ...   \n",
       " 11174  0.001074  0.001976  0.000593  0.013415  0.031193  0.009426  530185.0   \n",
       " 11175  0.001074  0.001976  0.000593  0.013415  0.031193  0.009426  530185.0   \n",
       " 11176  0.001074  0.001976  0.000593  0.013415  0.031193  0.009426  530185.0   \n",
       " 11177  0.001074  0.001976  0.000593  0.013415  0.031193  0.009426  530185.0   \n",
       " 11178  0.001074  0.001976  0.000593  0.013415  0.031193  0.009426  530185.0   \n",
       " \n",
       "        PollutionSource  \n",
       " 0         0.000000e+00  \n",
       " 1         0.000000e+00  \n",
       " 2         0.000000e+00  \n",
       " 3         0.000000e+00  \n",
       " 4         0.000000e+00  \n",
       " ...                ...  \n",
       " 11174     4.293418e+06  \n",
       " 11175     4.349303e+06  \n",
       " 11176     4.508231e+06  \n",
       " 11177     4.659403e+06  \n",
       " 11178     4.668251e+06  \n",
       " \n",
       " [10997 rows x 26 columns],\n",
       " 0        3.0\n",
       " 1        3.0\n",
       " 2        3.0\n",
       " 3        3.0\n",
       " 4        3.0\n",
       "         ... \n",
       " 11174    2.0\n",
       " 11175    2.0\n",
       " 11176    2.0\n",
       " 11177    2.0\n",
       " 11178    2.0\n",
       " Name: Pollution, Length: 10997, dtype: float64,\n",
       " Index(['pH值', '溶解氧', '电导率', '浊度', '高锰酸盐指数', '化学需氧量', '五日生化需氧量', '氨氮', '总磷',\n",
       "        '总氮', '铜', '锌', '氟化物', '硒', '砷', '汞', '镉', '六价铬', '铅', '氰化物', '挥发酚',\n",
       "        '石油类', '阴离子表面活性剂', '硫化物', 'timediff', 'PollutionSource'],\n",
       "       dtype='object'),\n",
       " array([3., 0., 1., 2.]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Data\n",
    "odata = pd.read_csv(\"../dt/songhua_river.csv\")\n",
    "odata['PollutionSource'] = odata['PollutionSource'].apply(lambda x: 0 if x == -1 else x)\n",
    "odata = odata[odata['SectionCode'].str.contains(\"_\")]\n",
    "label = odata['Pollution']\n",
    "label = label.apply(lambda x: 3 if x == 4 else x)\n",
    "data = odata.drop(['Unnamed: 0', 'Pollution', 'SectionCode'], axis=1)\n",
    "data, label, data.columns, label.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d5f85e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Preload \n",
    "to_tensor = transforms.ToTensor()\n",
    "normalize = transforms.Normalize((0.5, ), (0.5, ))\n",
    "\n",
    "transform = transforms.Compose([\n",
    "  to_tensor, # 转换为张量\n",
    "  normalize, # 归一化\n",
    "])\n",
    "\n",
    "X = (data.values - data.values.mean(axis=0)) / data.values.std(axis=0)\n",
    "y = label.values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "# 通过transform变为张量\n",
    "X_train = transform(X_train).float()\n",
    "X_test = transform(X_test).float()\n",
    "y_train = torch.tensor(y_train, dtype=torch.long)\n",
    "y_test = torch.tensor(y_test, dtype=torch.long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58355c99",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class MLP(nn.Module):\n",
    "\n",
    "  def __init__(self, input_size, hidden_size, output_size):\n",
    "    super(MLP, self).__init__()\n",
    "\n",
    "    self.relu = nn.ReLU()\n",
    "    self.lin_1 = nn.Linear(input_size, hidden_size)\n",
    "    self.lin_2 = nn.Linear(hidden_size, hidden_size)\n",
    "    self.lin_3 = nn.Linear(hidden_size, output_size)\n",
    "    self.dropout = nn.Dropout(p=0.2)\n",
    "\n",
    "  def forward(self, x):\n",
    "    x = self.lin_1(x)\n",
    "    x = self.relu(x)\n",
    "    x = self.dropout(x)\n",
    "    x = self.lin_2(x)\n",
    "    x = self.relu(x)\n",
    "    x = self.dropout(x)\n",
    "    x = self.lin_3(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2f13627d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model):\n",
    "  train_acc_list = []\n",
    "  train_loss_list = []\n",
    "  test_acc_list = []\n",
    "  test_loss_list = []\n",
    "  cost = nn.CrossEntropyLoss()\n",
    "  optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "  epochs = 30\n",
    "  for _ in trange(epochs):\n",
    "    sum_loss = 0\n",
    "    train_acc = 0\n",
    "    outputs = model(X_train).squeeze(0) # .argmax(dim=1)\n",
    "    # print(\"outputs: \", outputs.squeeze(0).shape, \"; y_train: \", y_train.shape)\n",
    "    # print(outputs.squeeze(0).argmax(dim=1))\n",
    "    # print(outputs, y_train)\n",
    "    optimizer.zero_grad()\n",
    "    loss = cost(outputs, y_train)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    _, id = torch.max(outputs.data, 1)\n",
    "    sum_loss += loss.item()\n",
    "    # print(id)\n",
    "    train_acc += (id == y_train).sum().item()\n",
    "    train_acc /= len(y_train)\n",
    "    train_loss_list.append(sum_loss)\n",
    "    train_acc_list.append(train_acc)\n",
    "    # print(f\"Epoch: {epoch + 1}, Loss: {sum_loss}, Train Acc: {train_acc}\")\n",
    "\n",
    "    with torch.no_grad():\n",
    "      model.eval()\n",
    "      outputs = model(X_test).squeeze(0)\n",
    "      _, predict = torch.max(outputs.data, 1)\n",
    "      test_acc = (predict == y_test).sum().item() / len(y_test)\n",
    "      test_loss = cost(outputs, y_test)\n",
    "      test_acc_list.append(test_acc)\n",
    "      test_loss_list.append(test_loss.item())\n",
    "  model.train()\n",
    "  return train_acc_list, train_loss_list, test_acc_list, test_loss_list\n",
    "\n",
    "def test(model):\n",
    "  model.eval()\n",
    "  test_acc = 0\n",
    "  outputs = model(X_test).squeeze(0)\n",
    "  _, id = torch.max(outputs.data, 1)\n",
    "  test_acc += (id == y_test).sum().item()\n",
    "  test_acc /= len(y_test)\n",
    "  print(f\"Test Acc: {test_acc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e035b3a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:00<00:00, 86.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Acc: 0.9990909090909091\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "input_size = 26\n",
    "hidden_size = 128\n",
    "output_size = 4\n",
    "batch_size = 64\n",
    "\n",
    "model = MLP(input_size, hidden_size, output_size)\n",
    "\n",
    "train_acc_list, train_loss_list, test_acc_list, test_loss_list = train(model)\n",
    "test(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "046b327b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(24, 10))\n",
    "\n",
    "axs[0].plot(train_acc_list, label=\"准确率\")\n",
    "axs[0].plot(train_loss_list, label=\"损失值\")\n",
    "axs[0].legend()\n",
    "axs[0].set_title(\"训练成果\")\n",
    "\n",
    "axs[1].plot(test_acc_list, label=\"准确率\")\n",
    "axs[1].plot(test_loss_list, label=\"损失值\")\n",
    "axs[1].legend()\n",
    "axs[1].set_title(\"测试成果\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2f78723",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
